ABSTRACT
Background: Much of researchers' efforts to foster wider implementation of educational innovations in STEM has focused on understanding and facilitating the implementation efforts of faculty. However, student engagement in blended learning and other innovations relies heavily on students' self-directed learning behaviors, implying that students are likely key actors in the implementation process. This paper explores the ways in which engineering students at multiple institutions experience the self-directed selection and implementation of blended learning resources in the context of their own studies. To accomplish this, it adopts a research perspective informed by Actor-Network Theory, allowing students themselves to be perceived as individual actors and implementors rather than a population that is implemented upon. Results: A thematic analysis was conducted in two parts. First, analysis identified sets of themes unique to the student experience at four participant institutions. Then, a second round of analysis identified and explored a subset of key actors represented in students' reported experiences across all institutions. The findings show clear similarities and differences in students' experiences of blended learning across the four institutions, with many themes echoing or building upon the results of prior research. Distinct institutional traits, the actions of the instructors, the components of the blended learning environment, and the unique needs and preferences of the students themselves all helped to shape students' self-directed learning experiences. Students' engagement decisions and subsequent implementations of blended learning resulted in personally appropriate, perhaps even idiosyncratic, forms of engagement with their innovative learning opportunities. Conclusion: The institutional implementation of blended learning, and perhaps other educational innovations, relies in part on the self-directed decision-making of individual students. This suggests that instructors too hold an additional responsibility: to act as facilitators of their students' implementation processes and as catalysts for growth and change in students' learning behaviors. Developing a greater understanding of students' implementation behaviors could inform the future implementation efforts of faculty and better empower students to succeed in the innovative classroom.
ABSTRACT
Understanding the brain is a fascinating challenge, captivating the scientific community and the public alike. The lack of effective treatment for most brain disorders makes the training of the next generation of neuroscientists, engineers and physicians a key concern. Over the past decade there has been a growing effort to introduce neuroscience in primary and secondary schools, however, hands-on laboratories have been limited to anatomical or electrophysiological activities. Modern neuroscience research labs are increasingly using computational tools to model circuits of the brain to understand information processing. Here we introduce the use of neurorobots - robots controlled by computer models of biological brains - as an introduction to computational neuroscience in the classroom. Neurorobotics has enormous potential as an education technology because it combines multiple activities with clear educational benefits including neuroscience, active learning, and robotics. We describe a 1-week introductory neurorobot workshop that teaches high school students how to use neurorobots to investigate key concepts in neuroscience, including spiking neural networks, synaptic plasticity, and adaptive action selection. Our do-it-yourself (DIY) neurorobot uses wheels, a camera, a speaker, and a distance sensor to interact with its environment, and can be built from generic parts costing about $170 in under 4 h. Our Neurorobot App visualizes the neurorobot's visual input and brain activity in real-time, and enables students to design new brains and deliver dopamine-like reward signals to reinforce chosen behaviors. We ran the neurorobot workshop at two high schools (n = 295 students total) and found significant improvement in students' understanding of key neuroscience concepts and in students' confidence in neuroscience, as assessed by a pre/post workshop survey. Here we provide DIY hardware assembly instructions, discuss our open-source Neurorobot App and demonstrate how to teach the Neurorobot Workshop. By doing this we hope to accelerate research in educational neurorobotics and promote the use of neurorobots to teach computational neuroscience in high school.
